{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:JYKU46NXHWOXO47O57STNVR4DL","short_pith_number":"pith:JYKU46NX","schema_version":"1.0","canonical_sha256":"4e154e79b73d9d7773eeefe536d63c1af2974a56d3a597b2c27a4b83b02bc743","source":{"kind":"arxiv","id":"2207.10766","version":1},"attestation_state":"computed","paper":{"title":"Benchmarks for a Global Extraction of Information from Deeply Virtual Exclusive Scattering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"hep-ph","authors_text":"Brandon Kriesten, Huey-Wen Lin, Jake Grigsby, Joshua Hoskins, Manal Almaeen, Simonetta Liuti, Yaohang Li","submitted_at":"2022-07-21T21:34:23Z","abstract_excerpt":"We develop a framework to establish benchmarks for machine learning and deep neural networks analyses of exclusive scattering cross sections (FemtoNet). Within this framework we present an extraction of Compton form factors for deeply virtual Compton scattering from an unpolarized proton target. Critical to this effort is a study of the effects of physics constraint built into machine learning (ML) algorithms. We use the Bethe-Heitler process, which is the QED radiative background to deeply virtual Compton scattering, to test our ML models and, in particular, their ability to generalize inform"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2207.10766","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"hep-ph","submitted_at":"2022-07-21T21:34:23Z","cross_cats_sorted":[],"title_canon_sha256":"ae756e4f749766adfc725cf02c097f4fa4ccce4bba056d3b6b46f8d975a5f1a0","abstract_canon_sha256":"ed1bf9f73ca5aa0a07f8e9cb7b3d7e00e76333dc15d0b278524dad097437515f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:42:39.128429Z","signature_b64":"oXumPIeyMxVwzs8qVuifCSeqUy7Q8xik7aYeDVf6g2kgamkjHIzCG8QlFilkCRyWTbRrECjV6ufMUyiWFU0PCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4e154e79b73d9d7773eeefe536d63c1af2974a56d3a597b2c27a4b83b02bc743","last_reissued_at":"2026-07-05T04:42:39.127956Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:42:39.127956Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Benchmarks for a Global Extraction of Information from Deeply Virtual Exclusive Scattering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"hep-ph","authors_text":"Brandon Kriesten, Huey-Wen Lin, Jake Grigsby, Joshua Hoskins, Manal Almaeen, Simonetta Liuti, Yaohang Li","submitted_at":"2022-07-21T21:34:23Z","abstract_excerpt":"We develop a framework to establish benchmarks for machine learning and deep neural networks analyses of exclusive scattering cross sections (FemtoNet). Within this framework we present an extraction of Compton form factors for deeply virtual Compton scattering from an unpolarized proton target. Critical to this effort is a study of the effects of physics constraint built into machine learning (ML) algorithms. We use the Bethe-Heitler process, which is the QED radiative background to deeply virtual Compton scattering, to test our ML models and, in particular, their ability to generalize inform"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.10766","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2207.10766/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2207.10766","created_at":"2026-07-05T04:42:39.128013+00:00"},{"alias_kind":"arxiv_version","alias_value":"2207.10766v1","created_at":"2026-07-05T04:42:39.128013+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.10766","created_at":"2026-07-05T04:42:39.128013+00:00"},{"alias_kind":"pith_short_12","alias_value":"JYKU46NXHWOX","created_at":"2026-07-05T04:42:39.128013+00:00"},{"alias_kind":"pith_short_16","alias_value":"JYKU46NXHWOXO47O","created_at":"2026-07-05T04:42:39.128013+00:00"},{"alias_kind":"pith_short_8","alias_value":"JYKU46NX","created_at":"2026-07-05T04:42:39.128013+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.09152","citing_title":"Constraining DVCS Compton Form Factors Using Lattice QCD informed Neural Network","ref_index":23,"is_internal_anchor":false},{"citing_arxiv_id":"2605.06994","citing_title":"Neural Network Representation of Generalized Parton Distributions (NNGPD)","ref_index":7,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JYKU46NXHWOXO47O57STNVR4DL","json":"https://pith.science/pith/JYKU46NXHWOXO47O57STNVR4DL.json","graph_json":"https://pith.science/api/pith-number/JYKU46NXHWOXO47O57STNVR4DL/graph.json","events_json":"https://pith.science/api/pith-number/JYKU46NXHWOXO47O57STNVR4DL/events.json","paper":"https://pith.science/paper/JYKU46NX"},"agent_actions":{"view_html":"https://pith.science/pith/JYKU46NXHWOXO47O57STNVR4DL","download_json":"https://pith.science/pith/JYKU46NXHWOXO47O57STNVR4DL.json","view_paper":"https://pith.science/paper/JYKU46NX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2207.10766&json=true","fetch_graph":"https://pith.science/api/pith-number/JYKU46NXHWOXO47O57STNVR4DL/graph.json","fetch_events":"https://pith.science/api/pith-number/JYKU46NXHWOXO47O57STNVR4DL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JYKU46NXHWOXO47O57STNVR4DL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JYKU46NXHWOXO47O57STNVR4DL/action/storage_attestation","attest_author":"https://pith.science/pith/JYKU46NXHWOXO47O57STNVR4DL/action/author_attestation","sign_citation":"https://pith.science/pith/JYKU46NXHWOXO47O57STNVR4DL/action/citation_signature","submit_replication":"https://pith.science/pith/JYKU46NXHWOXO47O57STNVR4DL/action/replication_record"}},"created_at":"2026-07-05T04:42:39.128013+00:00","updated_at":"2026-07-05T04:42:39.128013+00:00"}